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Control Software for Integrated CW Radar Module

NASA’s Jet Propulsion Laboratory, Pasadena, California This software controls the behavior of a miniaturized microwave radar module. It controls the hardware, digitizes raw samples from the analog output of the module, and applies DSP (digital signal processing) algorithms to the data stream to reduce the bandwidth and data rate. It also implements an automatic calibration algorithm to adjust the I/Q (in-phase and quadrature) values in the cancellation path to remove a large unchanging signal. The software implements a variety of commands to control the behavior of the system, and provides for synchronization of multiple modules. It encodes the digital data in a format suitable for serial ports.

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Furuno Radar/SureTrak Interface Software

Goddard Space Flight Center, Greenbelt, Maryland The Wallops Flight Facility Launch Range has a need to interface data from ship surveillance Furuno radar to an existing surveillance display system (SureTrak). SureTrak is a multi-sensor waterway and air surveillance system. The display of Furuno radar data by the SureTrak system will be used for risk analysis purposes prior to rocket launches. The capability did not exist within the SureTrak system to ingest data from the Furuno radar. This software application was developed to provide the needed data interface capability within the SureTrak system. In addition to providing a data interface to SureTrak, the software application will also provide a data interface to another software application that performs probability of impact calculations on the ships reported by the Furuno radar.

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An Empirical Metric of Individual Datapoint Utility Given Ample Metadata as Applied to the OCO-2 Flight System

This method constructs new warn levels for metadata-rich data sources. NASA’s Jet Propulsion Laboratory, Pasadena, California Traditionally, quality flags provided a binary yes/no estimation of a datapoint’s utility. However, in modern instrumentation, significant auxiliary information for each datapoint can be obtained. This permits prediction of more than a binary estimate of good or bad data. Further, the physical confounding forces that obscure an observation’s utility are themselves rarely binary, such as the example of clouds with varying thickness from insignificant to entirely opaque. In this method, many different increasingly stringent filters are created allowing less and less data through, while attempting to minimize an error metric. This metric can be compared with select “truth” systems such as ground observations or regions of the Earth where the truth is believed to be predictable and known. For each sounding, the number of these filters that reject the observation in question becomes an estimate of its data quality: larger values mean most filters reject the sounding, while smaller values mean most filters accept the sounding. This integer, ranging from 0 to 19, is called the Warn Level. Instead of a binary yes/no data quality flag, this instead provides a data ordering paradigm with “better” and “worse” data. Warn Levels can be developed for any metadata-rich datasource with a functional error metric to help guide researchers to superior, tunable data filtration.

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Gravitational Compensation Onboard a Comsat

NASA’s Jet Propulsion Laboratory, Pasadena, California This technique for compensating the gravitational attraction experienced by a test-mass freely floating onboard a satellite is new, and solves an important problem that all gravitational wave missions face. Its application to the geostationary Laser Interferometer Space Antenna (gLISA) mission concept addresses and completely solves an important noise source: the gravity-gradient noise.

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Open Scheduling and Planning Interface for Exploration (Open SPIFe)

Ames Research Center, Moffett Field, California In order to accomplish mission objectives, NASA must be able to plan and sequence assets (spacecraft and astronauts) in a short amount of time. Planning is a complex process that involves reasoning about thousands of constraints and uncertain conditions in order to produce a sequence of commands for execution onboard a spacecraft. The sequence produced must be nearly defect-free: defects introduce risk to both human and robotic assets. The NASA domain in particular requires reasoning about complex constraints and interactions between planned activities, as well as consideration for various uncertain events in the execution environment that can change the parameters of the planning space in near real time.

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AMMOS-PDS Pipeline Service (APPS) — Label Design Tool (LDT)

NASA’s Jet Propulsion Laboratory, Pasadena, California A software program builds PDS4 science product label (metadata) and automatically generates its description as part of the software interface specification (SIS) document. This software allows the mission system engineer to interact programmatically with the PDS4 information model, and retrieve science product metadata information via graphical user interfaces (GUIs). This capability will greatly improve the processes of creating and generating software interface specification documents for science instruments. Given that PDS4 is a newly defined standard, most of the work that is simplified by this software suite is being done manually. This improvement allows the definition and design of PDS4 science data archive models for generating PDS4 compliant labels.

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Activity Model Problem Translator

NASA’s Jet Propulsion Laboratory, Pasadena, California The Problem Translator is a software program that translates functional Unified Modeling Language (fUML) activity models into a behavior-based computational problem representation language called Behavior XML (BXML). The BXML translation may then be solved by engines such as the Behavior and Analysis Engine. The translation software uniquely adds timing and richer problem-solving semantics to the standard fUML by translating activity models, augmented with timing and other constraints on events and state variables, into a tool-agnostic behavioral XML specification.

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